I’d add data/task understanding as a separate mid layer. Most papers I know break in the transition of high to mid.

This is why I personally love TMLR. If it's correct and well-written let's publish. The interesting papers are the ones the community actively recognizes in their work, e.g. citing, extending, turning into products, etc. (independent process of publication).

I agree with most of your thread, but classifying "uninteresting work" is quite hard nowadays. Papers became this "hype-seeking" game, where out of the 10 hyped papers of the month, at most 1 survives further investigation of the results. And even if we think we're immune to this, what is interest?

We're at ICML, drop us a line if you're excited about this direction.

📄 Paper: arxiv.org/abs/2507.02083
💻 Code: github.com/h4duan/SciGym
🌍 Website: h4duan.github.io/scigym-bench...
🗂️ Dataset: huggingface.co/datasets/h4d...

Also, I see ITCS more like a “out of the box”, “bold” idea or even new area, I don’t see the papers having simplicity as a goal, but just my experience.

Mhm, I agree with the idealistic part, I certainly have seen the same. But I know quite a few papers that are aligned w the call, tbh this happens in any venue. I think the message and the openness to this kind of paper is important though

this is not my area, but if you think of it in terms of a randomized algorithm (BPP,PP), the hard part is usually the generation, at least for the algorithms we tend to design. e.g. Schwartz-Zippel Lemma. (Although in theory you can have the "hard part" in verification for any problem)

It takes 1 terrible paper for knowledgeable people to stop reading all your papers, this risk is often not accounted for

Maybe check Cat s22, it gives you the basics, eg whatsapp+gps and nothing else

it just sounds like "see you three times" ;) it's like some people named "Sinho" that is often confused with portuguese/brazilians; but from what I heard it's a variation of Singh (not sure though)

One simple way to reason about this: treatment assignment guarantees you have the right P(T|X). Self-selection changes P(X), a different quantity. Looking at your IPW estimator you can see that changing P(X) will bias regardless of P(T|X).

this general idea of using an external world/causal model given by a human and using the LM only for inference is really cool ---it's also the insight behind our work in NATURAL. Do you guys think it's possible to write a more general software for the interface DAG->LLM_inference->estimate?

Oh gotcha. I think it’s just super cheesy to quote feynman at this point haha but it’s a good philosophy to embrace

In what contexts do you think it’s misused? Just curious, I’m a big fan and might be overusing it 😅

if you're feeling uninspired and getting nan's everywhere, you can give your codebase, describe the problem and ask for suggestions to try or debug. I think of it more as a debugger assistant than a code generator.